| dc.contributor.advisor | 余清祥<br>張源俊 | zh_TW |
| dc.contributor.advisor | <br> | en_US |
| dc.contributor.author (Authors) | 陳炳霖 | zh_TW |
| dc.creator (作者) | 陳炳霖 | zh_TW |
| dc.date (日期) | 2003 | en_US |
| dc.date.accessioned | 2009-09-14 | - |
| dc.date.available | 2009-09-14 | - |
| dc.date.issued (上傳時間) | 2009-09-14 | - |
| dc.identifier (Other Identifiers) | G0091354025 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/30890 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 統計研究所 | zh_TW |
| dc.description (描述) | 91354025 | zh_TW |
| dc.description (描述) | 92 | zh_TW |
| dc.description.abstract (摘要) | 普適提(Boosting)是近年來發展迅速且廣泛被應用於分類問題的方法之一，其特點是利用精確度較低或是較粗糙的分類方法(weak learner)為基礎，經由多次反覆分類之後，將其結果合併而提升分類的精確度。本研究為探討若是SVM, KNN, LDA等分類基底時，普適提是否仍然能夠達到改進的效果，或是會造成模型過適的情況(overfitting)。我們發現到普適提在訓練資料集(training set)與測試資料集(testing set)的解釋變數獨立且同分佈(independent identically ditributed)產生的情況下，無論用任何一種分類法則皆有造成模型過適的可能；但是，若訓練資料集與測試資料集的相關程度很高的情況下，則不會發生模型過適的情況，因此，欲探討不同相關係數以及分布類似的程度對測試資料集的結果有何影響。 | zh_TW |
| dc.description.tableofcontents | 1 緒論 1.1 簡介 1.2 研究目標與方法2 普適提演算法介紹 2.1 普是提演算法流程 2.1.1 AdaBoost 2.1.2 LogitBoost 2.1.3 其它Boosting 2.2 classifier的簡介 2.2.1 SVM 2.2.2 KNN 2.2.3 LDA3 模擬研究與結果分析 3.1 研究方法與設定 3.2 Independent identically distributed data 3.2.1 Master Card Type 3.2.2 同心圓 3.2.3 棋盤格 3.2.4 Micky Mouse Type 3.2.5 Symmetric 3.3 Correlated data 3.3.1 High Correlation 3.3.2 Small Error 3.3.3 相關係數與誤差標準差的選擇 3.4 小結4 結論與建議 4.1 本研究之結論 4.2 後續研究之建議參考文獻附錄 | zh_TW |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0091354025 | en_US |
| dc.subject (關鍵詞) | 普適提 | zh_TW |
| dc.title (題名) | 普適提演算法比較 | zh_TW |
| dc.type (資料類型) | thesis | en |
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